Interesting comment regarding Conal Elliott. I've always thought there is some similarity between probabilistic programming (specify a probabilistic model as a graph), functional reactive programming (specify some reactivity as a graph) and deep learning (specify some linear algebra / calculus / optimization operations as a graph). Too bad the word "graphical programming" would be interpreted as "visual programming" (or programming using plots and charts!) and not programming using an explicit graph structure.
Conal Elliott has done a lot of work in this area about 10 years ago. His work is beautiful but maybe before it’s time.